To Detect Outlier for Categorical Data Streaming

Instant identification of outlier patterns is very important in modern-day engineering problems such as credit card fraud detection and network intrusion detection. Most previous studies focused on finding outliers that are hidden in numerical datasets. Unfortunately, those outlier detection methods were not directly applicable to real life transaction databases. Outlier detection methods are divided into transaction specific and non transaction specific outlier detection methods, in this paper we are going to focus mainly on transaction specific methods and detect outlier transactions from transactional databases e.g. purchase of the data at the store, customer dataset at a company. Here we are going to compare two transaction specific methods and find efficient method from them. KEYWORDSOutlier, Categorical Data Streaming, and Transaction based method, association rule, and frequent pattern. ——————————  ——————————